

import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__a    :Optional[int]      =			logging.get_logger(__name__)

__a    :Optional[int]      =			{
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class   _a					(			snake_case_				):




      """simple docstring"""

      _lowerCamelCase       :			Dict      		=       'detr'
      _lowerCamelCase       :			Union[str, Any]      		=       ['past_key_values']
      _lowerCamelCase       :			Optional[int]      		=       {
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
      }





      def __init__(   self							:				int						,			UpperCAmelCase							:				List[Any]=True						,			UpperCAmelCase							:				Union[str, Any]=None						,			UpperCAmelCase							:				Dict=3						,			UpperCAmelCase							:				List[Any]=100						,			UpperCAmelCase							:				Union[str, Any]=6						,			UpperCAmelCase							:				Optional[int]=2048						,			UpperCAmelCase							:				str=8						,			UpperCAmelCase							:				Optional[Any]=6						,			UpperCAmelCase							:				List[Any]=2048						,			UpperCAmelCase							:				Tuple=8						,			UpperCAmelCase							:				List[Any]=0.0						,			UpperCAmelCase							:				Any=0.0						,			UpperCAmelCase							:				Tuple=True						,			UpperCAmelCase							:				List[str]="relu"						,			UpperCAmelCase							:				List[str]=256						,			UpperCAmelCase							:				str=0.1						,			UpperCAmelCase							:				Optional[Any]=0.0						,			UpperCAmelCase							:				Any=0.0						,			UpperCAmelCase							:				Optional[int]=0.02						,			UpperCAmelCase							:				Tuple=1.0						,			UpperCAmelCase							:				Any=False						,			UpperCAmelCase							:				Union[str, Any]="sine"						,			UpperCAmelCase							:				str="resnet50"						,			UpperCAmelCase							:				List[Any]=True						,			UpperCAmelCase							:				int=False						,			UpperCAmelCase							:				Dict=1						,			UpperCAmelCase							:				Tuple=5						,			UpperCAmelCase							:				Optional[int]=2						,			UpperCAmelCase							:				Dict=1						,			UpperCAmelCase							:				Union[str, Any]=1						,			UpperCAmelCase							:				Tuple=5						,			UpperCAmelCase							:				Optional[Any]=2						,			UpperCAmelCase							:				Dict=0.1						,			**UpperCAmelCase							:				int						,			):
            if backbone_config is not None and use_timm_backbone:
                  raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."	)

            if not use_timm_backbone:
                  if backbone_config is None:
                        logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."	)
                        A_						  =				CONFIG_MAPPING["resnet"](out_features=["stage4"]	)
                  elif isinstance(UpperCAmelCase						,			UpperCAmelCase	):
                        A_						  =				backbone_config.get("model_type"	)
                        A_						  =				CONFIG_MAPPING[backbone_model_type]
                        A_						  =				config_class.from_dict(UpperCAmelCase	)
                  # set timm attributes to None
                  A_  ,     A_  ,     A_						  =				None, None, None

            A_						  =				use_timm_backbone
            A_						  =				backbone_config
            A_						  =				num_channels
            A_						  =				num_queries
            A_						  =				d_model
            A_						  =				encoder_ffn_dim
            A_						  =				encoder_layers
            A_						  =				encoder_attention_heads
            A_						  =				decoder_ffn_dim
            A_						  =				decoder_layers
            A_						  =				decoder_attention_heads
            A_						  =				dropout
            A_						  =				attention_dropout
            A_						  =				activation_dropout
            A_						  =				activation_function
            A_						  =				init_std
            A_						  =				init_xavier_std
            A_						  =				encoder_layerdrop
            A_						  =				decoder_layerdrop
            A_						  =				encoder_layers
            A_						  =				auxiliary_loss
            A_						  =				position_embedding_type
            A_						  =				backbone
            A_						  =				use_pretrained_backbone
            A_						  =				dilation
            # Hungarian matcher
            A_						  =				class_cost
            A_						  =				bbox_cost
            A_						  =				giou_cost
            # Loss coefficients
            A_						  =				mask_loss_coefficient
            A_						  =				dice_loss_coefficient
            A_						  =				bbox_loss_coefficient
            A_						  =				giou_loss_coefficient
            A_						  =				eos_coefficient
            super().__init__(is_encoder_decoder=UpperCAmelCase						,			**UpperCAmelCase	)





      @property
      def 		__A	(   self							:				Dict	):
            return self.encoder_attention_heads





      @property
      def 		__A	(   self							:				List[Any]	):
            return self.d_model





      @classmethod
      def 		__A	(   cls							:				Dict						,			UpperCAmelCase							:				PretrainedConfig						,			**UpperCAmelCase							:				Any	):
            return cls(backbone_config=UpperCAmelCase						,			**UpperCAmelCase	)







      def 		__A	(   self							:				int	):
            A_						  =				copy.deepcopy(self.__dict__	)
            if output["backbone_config"] is not None:
                  A_						  =				self.backbone_config.to_dict()
            A_						  =				self.__class__.model_type
            return output




class   _a					(			snake_case_				):




      """simple docstring"""

      _lowerCamelCase       :			Tuple      		=       version.parse('1.11'				)





      @property
      def 		__A	(   self							:				List[str]	):
            return OrderedDict(
                [
                    ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                    ("pixel_mask", {0: "batch"}),
                ]	)





      @property
      def 		__A	(   self							:				Tuple	):
            return 1E-5







      @property
      def 		__A	(   self							:				Union[str, Any]	):
            return 12
'''simple docstring'''




def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							)      ->       float:
    if digit_amount > 0:
        return round(number - int(UpperCamelCase__							)							,    UpperCamelCase__							)
    return number - int(UpperCamelCase__							)


if __name__ == "__main__":
  print(decimal_isolate(1.53, 0))
  print(decimal_isolate(35.345, 1))
  print(decimal_isolate(35.345, 2))
  print(decimal_isolate(35.345, 3))
  print(decimal_isolate(-14.789, 3))
  print(decimal_isolate(0, 2))
  print(decimal_isolate(-14.123, 1))
  print(decimal_isolate(-14.123, 2))
  print(decimal_isolate(-14.123, 3))
